Where: Math 1308

Speaker: Yuan Liao, UMCP

Where: MATH B0421

Speaker: Dr. Yuan Liao (Dept. of Math, UMCP) -

Abstract: We will start to discuss linear regression model with many regressors. The penalized least squares will be introduced, with a focus on the l_1 penalty, often known as ``Lasso". Intuitions and algorithms are to be discussed. If time permits, statistical properties will also be introduced.

Where: MATH B0421

Speaker: Dr. Yuan Liao (UMCP) -

Where: MTH B0421

Speaker: Prof. Abram Kagan (Department of Mathematics, UMCP) -

Where: MTH 1313

Speaker: Xia Li and Yuan Liao, UMCP () -

Abstract: We shall see that LASSO is not variable selection consistent in general when the important and unimportant regressors are correlated. Intuitively, it puts equal weights to all the coefficients. A more "adaptive" penalty should penalize coefficients unequally. We shall discuss weighted L_1 penalized regression and its "oracle properties".

Where: MTH 1313

Speaker: David Shaw (UMCP) -

Where: MTH B0421

Speaker: Prof. Paul Smith (Department of Mathematics, UMCP) -

Where: MATH B0421

Speaker: Paul Smith (UMCP) -

Where: MTH 1313

Speaker: Yue Tian (UMCP) -

Where: MTH 1313

Speaker: Hechao Sun (Department of Mathematics, UMCP) -

Abstract: This paper is written by Zou and Hastie (2005), with 2327 citations on Google scholar. They propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together.The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the pn case. An algorithm called LARS-EN is proposed for computing elastic net regularization paths efﬁciently, much like algorithm LARS does for the lasso.

Where: B0421

Speaker: Paul Smith, Abram Kagan (UMCP) -

Where: MTH 1313

Speaker: () -

Where: MTH 1313

Speaker: Prof. Abram Kagan (Department of Mathematics, UMCP) -

Where: MTH 1313

Speaker: Prof. Abram Kagan (Dept. of Statistics, UMCP) -

Where: MTH 1313

Speaker: Prof. Abram Kagan (UMCP) -

Where: MTH 1313

Speaker: Hechao Sun (Dept. of Math, UMCP) -

Where: MTH 1313

Speaker: Xia Li (Dept. of Math., Univ. of Maryland) -

Where: MTH 1313

Speaker: David Shaw (Dept. of Math, UMCP) -

Where: MATH 1313

Speaker: Prof. Paul Smith (Dept. of Math., Univ. of Maryland) -

Where: MATH 1313

Speaker: Prof. Abram Kagan (Dept. of Math, Univ. of Maryland) -